{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.数据读取"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>hour</th>\n",
       "      <th>pm2.5</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>PRES</th>\n",
       "      <th>Iws</th>\n",
       "      <th>Is</th>\n",
       "      <th>Ir</th>\n",
       "      <th>cbwd_NE</th>\n",
       "      <th>cbwd_NW</th>\n",
       "      <th>cbwd_SE</th>\n",
       "      <th>cbwd_cv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>-16</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>1.79</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>148.0</td>\n",
       "      <td>-15</td>\n",
       "      <td>-4.0</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>2.68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>2</td>\n",
       "      <td>159.0</td>\n",
       "      <td>-11</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>3.57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>3</td>\n",
       "      <td>181.0</td>\n",
       "      <td>-7</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>1022.0</td>\n",
       "      <td>5.36</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>4</td>\n",
       "      <td>138.0</td>\n",
       "      <td>-7</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>1022.0</td>\n",
       "      <td>6.25</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>5</td>\n",
       "      <td>109.0</td>\n",
       "      <td>-7</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>1022.0</td>\n",
       "      <td>7.14</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>6</td>\n",
       "      <td>105.0</td>\n",
       "      <td>-7</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>1023.0</td>\n",
       "      <td>8.93</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>7</td>\n",
       "      <td>124.0</td>\n",
       "      <td>-7</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>1024.0</td>\n",
       "      <td>10.72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>8</td>\n",
       "      <td>120.0</td>\n",
       "      <td>-8</td>\n",
       "      <td>-6.0</td>\n",
       "      <td>1024.0</td>\n",
       "      <td>12.51</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2010-01-02</td>\n",
       "      <td>9</td>\n",
       "      <td>132.0</td>\n",
       "      <td>-7</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>1025.0</td>\n",
       "      <td>14.30</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  hour  pm2.5  DEWP  TEMP    PRES    Iws  Is  Ir  cbwd_NE  \\\n",
       "0  2010-01-02     0  129.0   -16  -4.0  1020.0   1.79   0   0        0   \n",
       "1  2010-01-02     1  148.0   -15  -4.0  1020.0   2.68   0   0        0   \n",
       "2  2010-01-02     2  159.0   -11  -5.0  1021.0   3.57   0   0        0   \n",
       "3  2010-01-02     3  181.0    -7  -5.0  1022.0   5.36   1   0        0   \n",
       "4  2010-01-02     4  138.0    -7  -5.0  1022.0   6.25   2   0        0   \n",
       "5  2010-01-02     5  109.0    -7  -6.0  1022.0   7.14   3   0        0   \n",
       "6  2010-01-02     6  105.0    -7  -6.0  1023.0   8.93   4   0        0   \n",
       "7  2010-01-02     7  124.0    -7  -5.0  1024.0  10.72   0   0        0   \n",
       "8  2010-01-02     8  120.0    -8  -6.0  1024.0  12.51   0   0        0   \n",
       "9  2010-01-02     9  132.0    -7  -5.0  1025.0  14.30   0   0        0   \n",
       "\n",
       "   cbwd_NW  cbwd_SE  cbwd_cv  \n",
       "0        0        1        0  \n",
       "1        0        1        0  \n",
       "2        0        1        0  \n",
       "3        0        1        0  \n",
       "4        0        1        0  \n",
       "5        0        1        0  \n",
       "6        0        1        0  \n",
       "7        0        1        0  \n",
       "8        0        1        0  \n",
       "9        0        1        0  "
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_csv ='pm25_train.csv'\n",
    "train_data = pd.read_csv(train_csv)\n",
    "test_csv ='pm25_test.csv'\n",
    "test_data = pd.read_csv(test_csv)\n",
    "train_data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.观察数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date       0\n",
       "hour       0\n",
       "pm2.5      0\n",
       "DEWP       0\n",
       "TEMP       0\n",
       "PRES       0\n",
       "Iws        0\n",
       "Is         0\n",
       "Ir         0\n",
       "cbwd_NE    0\n",
       "cbwd_NW    0\n",
       "cbwd_SE    0\n",
       "cbwd_cv    0\n",
       "year       0\n",
       "month      0\n",
       "day        0\n",
       "week       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " 可以看到date时间字段，尝试将其转化为年月日周 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "def getYear(dt):\n",
    "    t = time.strptime(dt, '%Y-%m-%d')\n",
    "    return t.tm_year\n",
    "def getMonth(dt):\n",
    "    t = time.strptime(dt, '%Y-%m-%d')\n",
    "    return t.tm_mon \n",
    "def getDay(dt):\n",
    "    t = time.strptime(dt, '%Y-%m-%d')\n",
    "    return t.tm_mday\n",
    "def getWeek(dt):\n",
    "    t = time.strptime(dt,'%Y-%m-%d')\n",
    "    return t.tm_wday"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['year']=train_data['date'].apply(getYear)\n",
    "train_data['month']=train_data['date'].apply(getMonth)\n",
    "train_data['day']=train_data['date'].apply(getDay)\n",
    "train_data['week']=train_data['date'].apply(getWeek)\n",
    "\n",
    "test_data['year']=test_data['date'].apply(getYear)\n",
    "test_data['month']=test_data['date'].apply(getMonth)\n",
    "test_data['day']=test_data['date'].apply(getDay)\n",
    "test_data['week']=test_data['date'].apply(getWeek)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hour</th>\n",
       "      <th>pm2.5</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>PRES</th>\n",
       "      <th>Iws</th>\n",
       "      <th>Is</th>\n",
       "      <th>Ir</th>\n",
       "      <th>cbwd_NE</th>\n",
       "      <th>cbwd_NW</th>\n",
       "      <th>cbwd_SE</th>\n",
       "      <th>cbwd_cv</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>35746.000000</td>\n",
       "      <td>35746.000000</td>\n",
       "      <td>35746.00000</td>\n",
       "      <td>35746.000000</td>\n",
       "      <td>35746.000000</td>\n",
       "      <td>35746.000000</td>\n",
       "      <td>35746.000000</td>\n",
       "      <td>35746.000000</td>\n",
       "      <td>35746.000000</td>\n",
       "      <td>35746.000000</td>\n",
       "      <td>35746.000000</td>\n",
       "      <td>35746.000000</td>\n",
       "      <td>35746.000000</td>\n",
       "      <td>35746.000000</td>\n",
       "      <td>35746.000000</td>\n",
       "      <td>35746.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>11.509819</td>\n",
       "      <td>98.805097</td>\n",
       "      <td>1.77950</td>\n",
       "      <td>12.413641</td>\n",
       "      <td>1016.427614</td>\n",
       "      <td>23.717183</td>\n",
       "      <td>0.057489</td>\n",
       "      <td>0.203463</td>\n",
       "      <td>0.114223</td>\n",
       "      <td>0.320176</td>\n",
       "      <td>0.350137</td>\n",
       "      <td>0.215465</td>\n",
       "      <td>2012.044341</td>\n",
       "      <td>6.516813</td>\n",
       "      <td>15.705058</td>\n",
       "      <td>3.00221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.924821</td>\n",
       "      <td>92.006764</td>\n",
       "      <td>14.33629</td>\n",
       "      <td>12.165962</td>\n",
       "      <td>10.253854</td>\n",
       "      <td>48.847511</td>\n",
       "      <td>0.790715</td>\n",
       "      <td>1.485673</td>\n",
       "      <td>0.318086</td>\n",
       "      <td>0.466550</td>\n",
       "      <td>0.477019</td>\n",
       "      <td>0.411150</td>\n",
       "      <td>1.414755</td>\n",
       "      <td>3.454528</td>\n",
       "      <td>8.798033</td>\n",
       "      <td>2.15600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-40.00000</td>\n",
       "      <td>-19.000000</td>\n",
       "      <td>992.000000</td>\n",
       "      <td>0.450000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>-10.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1008.000000</td>\n",
       "      <td>1.790000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>2.00000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>1016.000000</td>\n",
       "      <td>5.370000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2012.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>4.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>15.00000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>1025.000000</td>\n",
       "      <td>21.920000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>5.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>23.000000</td>\n",
       "      <td>994.000000</td>\n",
       "      <td>28.00000</td>\n",
       "      <td>41.000000</td>\n",
       "      <td>1046.000000</td>\n",
       "      <td>565.490000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>6.00000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               hour         pm2.5         DEWP          TEMP          PRES  \\\n",
       "count  35746.000000  35746.000000  35746.00000  35746.000000  35746.000000   \n",
       "mean      11.509819     98.805097      1.77950     12.413641   1016.427614   \n",
       "std        6.924821     92.006764     14.33629     12.165962     10.253854   \n",
       "min        0.000000      0.000000    -40.00000    -19.000000    992.000000   \n",
       "25%        6.000000     29.000000    -10.00000      2.000000   1008.000000   \n",
       "50%       12.000000     73.000000      2.00000     14.000000   1016.000000   \n",
       "75%       18.000000    137.000000     15.00000     23.000000   1025.000000   \n",
       "max       23.000000    994.000000     28.00000     41.000000   1046.000000   \n",
       "\n",
       "                Iws            Is            Ir       cbwd_NE       cbwd_NW  \\\n",
       "count  35746.000000  35746.000000  35746.000000  35746.000000  35746.000000   \n",
       "mean      23.717183      0.057489      0.203463      0.114223      0.320176   \n",
       "std       48.847511      0.790715      1.485673      0.318086      0.466550   \n",
       "min        0.450000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        1.790000      0.000000      0.000000      0.000000      0.000000   \n",
       "50%        5.370000      0.000000      0.000000      0.000000      0.000000   \n",
       "75%       21.920000      0.000000      0.000000      0.000000      1.000000   \n",
       "max      565.490000     27.000000     36.000000      1.000000      1.000000   \n",
       "\n",
       "            cbwd_SE       cbwd_cv          year         month           day  \\\n",
       "count  35746.000000  35746.000000  35746.000000  35746.000000  35746.000000   \n",
       "mean       0.350137      0.215465   2012.044341      6.516813     15.705058   \n",
       "std        0.477019      0.411150      1.414755      3.454528      8.798033   \n",
       "min        0.000000      0.000000   2010.000000      1.000000      1.000000   \n",
       "25%        0.000000      0.000000   2011.000000      4.000000      8.000000   \n",
       "50%        0.000000      0.000000   2012.000000      7.000000     16.000000   \n",
       "75%        1.000000      0.000000   2013.000000     10.000000     23.000000   \n",
       "max        1.000000      1.000000   2014.000000     12.000000     31.000000   \n",
       "\n",
       "              week  \n",
       "count  35746.00000  \n",
       "mean       3.00221  \n",
       "std        2.15600  \n",
       "min        0.00000  \n",
       "25%        1.00000  \n",
       "50%        4.00000  \n",
       "75%        5.00000  \n",
       "max        6.00000  "
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.看下我们要预测的y的分布情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: 1.811953\n",
      "Kurtosis: 4.915487\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import norm\n",
    "sns.distplot(train_data['pm2.5'], fit=norm);\n",
    "print(\"Skewness: %f\" % train_data['pm2.5'].skew())\n",
    "print(\"Kurtosis: %f\" % train_data['pm2.5'].kurt())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们可以看到要预测的pm2.5值正态分布可能性检测，偏度是右偏的，峰度4.9左右，与正态分布相距较远。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "fig = plt.figure()\n",
    "res = stats.probplot(train_data['pm2.5'], plot=plt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们先看下要预测的pm2.5值线性分布可能性检测，probplot函数计算一个当前样本最可能的线性分布，\n",
    "            并用plt展示出来，我们可以直观的看到线性拟合程度并不好。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接下来我们做log转换，然后看看分布。0值做log转换会出错，我们看0值只有2条，drop掉"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>hour</th>\n",
       "      <th>pm2.5</th>\n",
       "      <th>DEWP</th>\n",
       "      <th>TEMP</th>\n",
       "      <th>PRES</th>\n",
       "      <th>Iws</th>\n",
       "      <th>Is</th>\n",
       "      <th>Ir</th>\n",
       "      <th>cbwd_NE</th>\n",
       "      <th>cbwd_NW</th>\n",
       "      <th>cbwd_SE</th>\n",
       "      <th>cbwd_cv</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19058</th>\n",
       "      <td>2012-09-28</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-5</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>139.48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2012</td>\n",
       "      <td>9</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19063</th>\n",
       "      <td>2012-09-28</td>\n",
       "      <td>15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-10</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>192.68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2012</td>\n",
       "      <td>9</td>\n",
       "      <td>28</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             date  hour  pm2.5  DEWP  TEMP    PRES     Iws  Is  Ir  cbwd_NE  \\\n",
       "19058  2012-09-28    10    0.0    -5  20.0  1020.0  139.48   0   0        0   \n",
       "19063  2012-09-28    15    0.0   -10  24.0  1017.0  192.68   0   0        0   \n",
       "\n",
       "       cbwd_NW  cbwd_SE  cbwd_cv  year  month  day  week  \n",
       "19058        1        0        0  2012      9   28     4  \n",
       "19063        1        0        0  2012      9   28     4  "
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[train_data['pm2.5']==0].head(10)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = train_data.drop(train_data[train_data['pm2.5'] == 0].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skewness: -0.364670\n",
      "Kurtosis: -0.535320\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_data['pm2.5_log'] = np.log(train_data['pm2.5']) \n",
    "sns.distplot(train_data['pm2.5_log'], fit=norm);\n",
    "print(\"Skewness: %f\" % train_data['pm2.5_log'].skew())\n",
    "print(\"Kurtosis: %f\" % train_data['pm2.5_log'].kurt())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res = stats.probplot(train_data['pm2.5_log'], plot=plt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到，经过log转换过后，分布好多了。基本上可以进行线性拟合了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.接下来我们看下各个特征间的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var = 'DEWP'\n",
    "data = pd.concat([train_data['pm2.5_log'], train_data[var]], axis=1)\n",
    "data.plot.scatter(x=var, y='pm2.5_log', ylim=(0, 10));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var = 'TEMP'\n",
    "data = pd.concat([train_data['pm2.5_log'], train_data[var]], axis=1)\n",
    "data.plot.scatter(x=var, y='pm2.5_log', ylim=(0, 10));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var = 'Iws'\n",
    "data = pd.concat([train_data['pm2.5_log'], train_data[var]], axis=1)\n",
    "data.plot.scatter(x=var, y='pm2.5_log', ylim=(0, 10));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cjotJYL4YoKWlOTLetXc4Mgb3zTGQTAYrJxPXTdinVrHYEU710mEtMh9XIngFcKy1dgD4njHmUuD7xpg/ttY+jfYprrnO1gZGJuZOe2dbeS5uaoDSe3tT6CM+cxFcng7d7MLxUGj2cjj24XODdX3GZ1imK5loRq8sd84lJijZZ8Ba+xFjzHHAdwpLT6hGUGONTdGTwQDe/ppj+eItsy14vOOMY2Mvh+tG393ZPPs0X4yr5XODdX3GZ1imK5loRq8sd66r85fAx4wx/2ytHSm8dl7h9W+hRFBzQ6F1gcIxwLfveCQQX/+TR9ho5raLaGyE6ZLJwo3BNefYcNhKtj2+PxCHuW70h67q4rHdw4G47HfxnGQVdYON4ybsSibLaUbvcpmJLfFyJYILgR+QX2zulQDW2hFjzGnA7cwtOSEpMjSai4zbmhoZLckEbc3BTHDuK49l83Vzo4bOPa28RnFQd0fgRn/QyuA/BZ+O3q/+8AG2PLQXyD9p5yaneO/rTpx93+sGG0PjpCuZuBJFPd1c45iJnZYhuRIf11pDDwHGGLM69PoTxpg/As5eysLJAoWXjQ7FTU0NkAvFJb7z04cDI36+c8fDXHDWCcFDOpaq9uno3f74QGRcq81tXAnHlSjqZZkLiKdjuxZDcqW2fNca2mWMWQusIz+KqGjnUhRKFqf3gHae2jcaiEtNhUYJheNtj/VHxuBeodTvhhO9u0KtNrdxJhxHraOeRg3F0bFdT7+v+PFKBMaYvwc+WuGtGaC8t1IS1dneFBmPTwVXEx0PrTY6PjEVGQP0rGiNjH12OXOtaVSrzW0Wu9RFPY0aqkWfitQf36EcFwJnAN+z1sY+ZLQwEuk3wAZr7Y64j581D5esM1QpDq1KXRa79k0G9wqlPrucudY0qtXmNotd6sKnDKnpR4ixTyVqSK7UF99EMAX8YImSQDP52cvlj4zLTENDsLm+wurOsXDNE3DFjQ3Bm39jhXKWjhiqFPtsbuO6KdVqc5vFLnXhU4Za9CPE0aficwyfIblSX3wTwVXAR4wxn7DWxv2X/1vgP4AXxnzc9IlhJpdrslgc2lqaGC1pDmprKW/9czX9+DzNX/2jbdz7YL5paMeuQSanpgOd0nHc5H1GuLgSTi1qHXGMxImjT6WeOr4lPr6JYAtwA3CJMSbwhrV2wX0ExpgTgdeT3/3sooUep17EMaM3vFdY+d5hi3fU2gPY+si+QBzmavrxuXk+sGNfZBwHnxEuroRTi1pHHCNx4uhTUUdwNvkmgs8CVwA/Jt9MtGjGmFbyTULvtNaOhhPMfFat6qS5efH907297hW0XZ9J4hiVRoZWe4xKTVSl77e0BOcVtLQ0ln3H8PhUWVz6mV7g0rf/WWSZcqHRSrmpmcAxnhme4Is33sfufSMcemAn7z7rRFZ2VfeUPDA8URb7nK+4XXjOC7gq4nfxKafrfKw9tDtwk197aHfZMVzl8DlGqSTO5UKonNF8E8Eqa+3fxvzdlwI/s9beXc0P9fePuD/kwadt0/WZej1GV3tzYEZyV3tz4P37S0byFOPwd/SEbsg9Xa1Vtxe3NjcGmqBamxsDx/jcDffNTjh78PEBhobHAxPOfMRRzric98oNs/9/fGScvpG5fhWfcl518/2ztYYHHx9gfHwyUGvYdPJ6xscnZ2thm05eX/F3jSqH7zGAuukjUDnnjj8f30RwqzHmDdba6+MpEgCvA9YYY84ree23xph3WWuvjfF7JMS1TEV4tGiF0aPOkSM+nY4bDl8VGD664YhVgfftYwORsY96GeHiU05ns00MQznqabkMiY9vImgCvmWM+TtgLyX/5Ky1pyzki621G0pjY8wMcIKGjyavgeA9pdLoJtfIkS9/7/dsfTTf5r9j1yBjE5O8f9NJgc+cfcqR7Ng9OLsMxdmnHBl4fyI0vyEce6mT9XF9RuJU08+gjl6phm8isMBHyA/xXAU8TekSmLKsdHe1BPYg7u5sqfoYv98ZnI38+x3ls5Ndy1C0tjQyWtIX0Rrqu/BRup4RULaeURzimCMQx+gmdfTKQvkmgiuAL5Af4dMGDJHv6P1gXAWx1mol05Q45IC2QCI45IDyKR6uG9d0qFc7HIP7xnVkaBe0I9dU35HmWs8IFn8jj+NJvHQoLVA2lBbczTY+s7lFKqlm1NCxwOnk1xdaD/wz8AngA0tTNFmosqad0PudbU2MlDxpd7YFR2E9FJqJHI4BPv/d37L9ibmlqp8ZHuOSN/3x3Ac8xsq6mjpcu6CBz/7K0esZweJv5D5P4q5kE0d/iM9sbpFKfBPBa8nvVLa7EFtjzG+Be1EiSJ3ujmb2l3QAh/cK8FlLyKU0CQBsfzwYtzQFO5krzElzbnDvmr0M8IlrfzNbe5kYGucT3/wNn7ngxbPvu9YzAv/JXovZocw1ec4nc7rK4TWbW6QC30bXMQILFwOwH1AjZAp1dwWbBLo7g3Fo+H5ZHIep6YbIGOb6CCYmp+kvbHBfqrsj2DdRqa9icCQXGZ99ypGs6m6jraWRVSvayjqkofKSEqWKNYYduwa5Z9serrlte+D9t5x6DBs3HMK61d1s3HBIxRE/rif+Yw7riYx9yuH6PUTm41sj+Bjwb8aY9wEPAmuBzYXXDi9+yFr7WPxFlGoNjgSfBAdHg7FrmYqVnS3sL7mhrlxAZ3FzE0yVDCdorvAvzfUkvnNPcPTMzt3lo2kaGxqYKmkCaQwNcSrtkB7PVd4XYbGdsD5DLsuabULxea86lmtu276o4aN1tfidpIpvIvh84X+3MldnLV5xFzHXLK0lqVMgFxpmmcsF42MO7+GBnQOBuNQlb/kjNl+7ZXZY58XnBId9Qr4qOR2KS3W0tTA+ORGIw1xNKmOh2cvhGOC4dasCy2Ecty44F8Gn/d51I49j2eWWpkZGSyblt4R2hYtj+GhaFr/zoYSULr6JoHyfQUmtcCdhOB4KNZ+E49Wruvjke14U+R2NTQ1Ml1QrGkPVipUrWgLLJqzsKk8ErifYrvYWJobm+gW6OsqP8fb/cRzXLHIpa5c4Fp07oKs1UMs6oLP6m56rHD4317QMMU1LQpI83x3KtBNZHWltaWKspBbQ2hqsqA2NTUbGPvL7D8yE4jk+m9e7nmAvftNJzpqJ6xjFDumRsRydFTqkvcTQh3LQAe083jcciKvl+l19bq5p2VRmsR30Ei/fGoHUiGvopw9X01B3Z3NgBE54VJGPFe0t9Jc8ra8IPa3H8RTtUzNx8ekjcPnSrffPzmfYsWuQ0bEJLnrDH82+73PTcm3kEwefp/04/i5xLJnt2uEujn0TxJ8SQYziuInHsVT1RG4qMvZ5Wnc5/7XP5fJv3cvk1AzNTQ2c/9rnBj/gUfA4Z+TOd4w4mkJ+v3MgMvZ5En869L3h2Eccw1jjWEsojiWzXYlx977hYNwfjH3OeRwJKyuUCDz53ORdN/GWJsg5xtbHoakRpqaCcak42ppv/68nZpeRzk3NcPuvnuDdZ8x1OvtcqHG0E9diP2HX1p1eE8piaI5z/a5xPO37iCO5uuaIDI5MRsY+ZYgjYWWl5qFE4CmOJ/WOthZyJR2GHe3VD8v02Uays72ViZKO2s55thqcTxw7XflcqHHcUJ4MDTENxz6reroudtc591naIY7muDiGscYhjuTqHgHVHGx6bA+eL58yxPHvKyud2koENdTR0kjp/NuO5uoXUWtvgdJpApVyic+InSg+F1AcW1XGsTZO3zNjkbHPsEzXxX7cET2BNY+OOyI43DY3GXxazeXKn/ZdzXE+zRhp6eiNY2lvV+1l9UFdgc711Qd1VfXz4D5f9TTKaqkpEcTI1fSzLzTlPxwD9HS1MFCy4FtP6CY+FprfHY5h8X0APjfoOLaqdN1AfS5U1+J2PjdYV3v0O04/PnKI6iN/GIyMwX0+fFZJrVXTj4srufr83Vy1F9fv6lP7cSWsehpltdSUCGI0OeWIQ43L4RjgPWc9L9AJ+57XPS/wvqu9Gtxr+LguVJ/Fy1zr2vhcqK4bqHt9nsLM4pLyhmcW+6zqWdZevT8Yu38X98J2rmP4rJJaL5vGxNGcEsfv6kpYtRplVQ+UCGLk6kdoJLjhc6WGIVcnrE+n9Xd++vDcOv9D43znjocDNz/XheqzeFkcT0qV9l8utS20p0E4BjhkVSdPPj0ciEv5rOo5GNqhLRy7+Cxs5+ZOJvWiFs0pcXTi1mqUVRyWutNaiaCGjl67km0lq3QevXZl2WdcF1FzU0Ng0/fm8EJBwLad+yJjV1OIa4w3uKvd7uWhITc1HRm7hsECPOvgrkAieNbB4WawOLr5o73tVRtojmg68hFPMkmHWjSnxDF8tJ6e9pe601qJoIZ81td33YS72oMdwZWWXRidmI6M9w8Hn/D3h574fSY/uardm6/fEqiVbL52S9nksMnQsqfhuKW5kamSyXDh9XnAfTEfc1hPoO290qqezU0Nge+ulFyjxPHUWEwmad9b2UctbrBxDB9Ny9O+j6WuZSkR1NDDT+6PjAGmQo3+4Xj1ge2BRLB6VfVLFYxOTEbGPvsAuJ62ngn9TDgG9yqoxx5xYOAp+dh1B5Ydw3UxbzrlKHbuHppdYmLTXx5V9pkPv/n5XP7Ne8lNzdDS1MCH3vz8eY9XSRzVdp/RTfWiFjfYWg0fTcs8gqWuZSkRFPiMz188dzOFK1k8tmckMgZobITp6WBcytWp7fOPzvW05dMgc2xoWOaxoWGZcTS5+CwxceSaHv714pdWfeyirIw1T5M4ho/6SMvfdqlrWUoEBW0tTYyWbKnVFhr7uTK069fKBUwIWr9mJfeXbOK+/lnlfQTuW6j7Frvu0O7ACJznhPb6bWlqCNQ0WkKP4q5RR+DTl9EYaPNvDk9vBs55hWHzdXP9COecagLvx/FkWYunwqyMNU+TOIaP1tM8gqWuZVU/o2mZmpkJPRaH4gvOPmH2htnS1MAFZweHIAKE928p38/FMUwGWB+6aVcbA+zcFWxa2PFUMA6vRhqOXTuHgXs3rK7QTNCujvLE6fM9i+Wza9fQyARX3Xw/H/naPVx18/0MhTbyydLOYK5zUU+KN89PXfgS3n3G8WU3edffFZbX3zaKagQF4YlZo6H4lp8/EhjWecudjwRWoASYDg3unA4NAXw4dEMOxwCEO2ZDsU+Hs2sryu6O5tl9fotxqaeeHo6Mwf20tba3M9CXsfbg4LBOiGcpYtdnfGbBuqr/cewMVgs+58s1mistTSG1EMc8grT0ISyWEoGnB0IrToZjgMbGJmAyFM+ZCC0PHY4BHnlqf2Ts05HrmmswMj4dGe/pH4mMwWN26dhUZAzuNtw4Fq7z6YR13RDi2BmsFnzOl2s0V1qaQmohjnkEyyVxKhEUuG6ePhu+P7u3kwef2B+IS7W2NDJast1ia0ullrnoiUU+yz/09rSxZ2A8EJdyLYDmWrYB5p4sR8ZydLaVP1n6XGSL3Su4WI6o2IerrIE+k44Fbm5TA675IQDDoapuOM7KkgoQT01uuSROJYKCI5+1goeeGgrE1Xo0tERCODahMe2mwph218Qin+UfwmuehedhudYici3bAMEny/Fc+ZOlz0UWx17BcSzv7Cpr6cijicGFbW5TC66lm8G9/WdamrlqIY6a3HJJnEoEBSOhpotw7MM1Qao4pr34ZFlpTLtrYtHe0Oqa4Rigs62R/qFgXMr1hHvgilZ2PzMeiMMGh8cjY5+LzNW++oqNa9nyYN/s+P5X/MnaCr9rsHbT2baA3dYcZa2Xpz7X0s0wt6FQ8ZwuZEMhmeOTOOuhH0GJoMC1nHFLaGmH8JBLcDcv+TxZutve3U/Arj4AVzlCE5HLYnDvWezD1b565c2/C3TQX/nd35XNTh4J/f4j49XXCFzq5anPtXQzxLOhkMzxeeCph3OqRODpgtcdz6e/vZUZ8jf4C84u/0OuPqiDP+wdDcSlXJuo+PB5AnY9Gbrakn2+w7XYWxxjtF3t2eD+XePYrrBemkvimP9RL7WfelIP51TzCDzddd/u2effGeCuLbvLPrO2tzsydtU6fPg8AYefBMOxqy3Z5zvCi7uF4zjGaHeFdt2ptK6S63ctluPBxwfmLYdL8anv0rdurDgePS3imP+q92Q5AAAMWklEQVSRlXHztVQP51Q1goKerlb6Stah7+kqH38dFYPHk2OoOaVszgDuJ1ifdmBXOZoagm09TQTjjtZGShd87qgwusk1Pt+n9uMqp7M92+MYy2m9GZc4xsXXS+2nntTDOVUiKBgIrcgZjn3a5l3thSvaW4I38QpPuK41fHzagV3l2DuYi4xHc9ORcel3zLvph0ftx1VOV3u2zzGW03ozLnGMi0/LnIjlpB7OqRJBgWvEj0+7uevJ8fWnrOeLtz4wG7/hZevLjuGa1RvHKAXXakU+G607296rqP3MV06fcfGumbJx7K9by41WovoyfGdR1/sIFqk9JQJPPu3mrifH//uDbYHPf+XWbWy8eE3gNdes3jhGKbhGQPnseexc673K2k+lcvqMi3fNlI1jeedab7RStNBZ1L7fk+bajdRW4onAGHM6cBlwGLAdeJ+19u5al8O1WqZP27zryTEXqmWEYyifIFZpwpiLqxwfcqy/7/Nk6fqOi990Epuv3TI7V+Hic06q+hg+59xnZNFipWWjlThqJvUwgkVqL9FEYIx5DvAN4DXAXcAbgVuNMeustTXdnaOjrYncyFwi6GgPrhN08AEdgbb5gys8FbqeHH3mInR3tgVuft1dweUhfKr2rl3OXOvv+zxZupa6WNHWwlHPPmC2nCvay2sErmP49Ie4ZsrGIY42XtffzWd7UJ/PuMRRu4ljSK6kS9I1gsOBL1tr7yzE3zLGfA44BvjvWhbkiNXdbH1kbm/fIw4NDv30WdrB9eToehKHuSfp4o5a4Sdpn6q9z1aTi+U6Hz7ldB3D50ncp+aRBq7z4fM3i+PvGkftxqcZS+pLoomgkACKSQBjzJ8CncCDtS7L4EguMu4LtdX3DZSvyDk0kpud0PPM0ARDY7nAk5LPTljFJ+ni01b4SdpnGOvToep+OHbxqXUMhPY5Dsc+TRDO5TI8WsVWr+oqm21ca3FMnvNZVdbnMy5p2exH0iXpGsEsY8xRwI3AP1hryzfzLVi1qpPm5qb53vbWG578dWh3oMq89tDuwGf6ngledH0D42XH+NBVvwh0XH7q+vu4+tJTqyrXV79xT+Bpq62tmQ+fu3E2Lt1FrRiHy+HzGd8y7Ng1WFYGcJ8v1/s+5fQpRzWqOQfVqNX58vlMLaSlHNVIe/mKkipnKhKBMeaFwPeBK621V0R9tr/C2vgLER5Bsunk9YyPT84+1W06eX3gM5WGXIaPsT8092D/8ETVI1We2D1YFpceo7O9CZ4hEIe/w+cziykDzJ2vYs0lfL5c59OnnD7l8LWUm8JXc77mOx+u8+lzjFrxKWuaLOXfPk5LXc6oJJN4IjDGnAr8G/ABa+1XkipHHJPB4ui4dHXm+Qzt9PnMYsoA7mGZPk0QrnLWy2JvcU7kiroZpGViUhxDciVdkh41dDRwA/BWa+2NSZbFxadT0rUkgmvyE8wtHFbc8CW8cJjPJimL3UjFp0PRtTGND1c562FqPmgiV1iWftflIukawTuBLuDrxpivl7z+SmvtXQmVqSKfTknXkgiuyU8QXCJ6PFe+RLTPUtaL3UjF58nTtTGND1c50/IE7KKJXEFZ+l2Xi6RHDX0Q+GCSZYhTHMsqx7FMcC1GdcQxkStLo0/0u0qaaRnqGMWxrHIcywTXYtlbn9/FpR6W542LfldJs6SbhlLD1a7p0+7paiv26WdwLZLm0x5di7Z118S3OM7XcqLfVeaThpnaDQtZyyZJfX2DVRf4vE/cUfbaVy85JRBfdfP9gfH7GzccEmjXdL0ft3oZkTFfOWt9vlzq/XymUb2UNe3lrNW10tvbPe9UdDUNFWgLv3jpfIn4ScO1okRQoC384qXzJeInDdeK+ggKtIVfvHS+RPzEsXnSYikRFGgLv+q491bW+RLxkYaZ2koEsiBailhk+VAfgSxIGjq4RCQeSgSyIGno4BKReKhpSBYkDR1cIhKPTCSC4uSxtE8sqSdp6OASkXhkIhH4zCwWEckq9RGIiGScEoGISMYpEYiIZJwSgYhIxmWis1ijhkRE5qcagYhIxikRiIhknBKBiEjGZaKP4MOfv4O+obn40JXwL+drQpmICGSkRlCaBAB270+mHCIiaZSJRCAiIvNTIhARybhMJIJDV0bHIiJZlonO4mLHsCaUiYiUy0SNQERE5qdEICKScUoEIiIZp0QgIpJxSgQiIhmnRCAiknFKBCIiGZf4PAJjzJ8AXwSOAX4DvNVa+3CypRIRyY5EawTGmHbgJmAzsAr4MfC1JMskIpI1SdcIXgrss9ZeC2CM+TjwfmPMBmvttri+ZOtDfXzmhq3MAA3A+9/wPI5f1xvX4ZedoZEJrrl9O30Do/T2dPCWU49hRUdr0sUSkSWSdB/BBmD2hm+tnQIeBo6L80uKSQBgBvj09VvjPPyyc83t27ln2x527Brknm17uOa27UkXSUSWUNI1gi5gJPTaCNA53w/09nY3VPslM8zmgWK8oOPUWm9vdyLfe8+2Pf8FbCyJ77m0t/uF830+qXJWS+WMX72UVeWMlnQiGAE6Qq91AkMVPrtg3/vk6am/6afJ9z55+rw3fRFZfpJuGtpGfrQQAMaYJuAowCZWIhGRjEm6RvBT4FBjzLnA9cAlwMPW2geSLZaISHYkWiOw1o4CrwIuAPYCLwc2JVkmEZGsaZiZmXF/SkRElq2k+whERCRhSfcR1EQ9LWNhjLkC+BtgsvDSmLX24ASLFGCMuQg4wVr71kJ8GvBpYC1wB3CetbYvuRLOqVDWG8g3RU4VPmKttS9IqHgYY04HLgMOA7YD77PW3p22cxpRzrSdz7OAj5M/bw8A77XW/mcar/+IsiZy/S/7GkEdLmNxIvBGa+2Kwn+pSALGmDZjzEfJn8fia6uB64B3AgcBu4BPJVPCOZXKWnAi8Ocl5zbJm9ZzgG8A5wM95G/8txpjDiNF5zSinN2k63yuA74O/LW1dgVwJfBvabz+5ytr4e1Erv9lnwgoWcbCWjtBPgsfb4zZkHC55nMicF/Shajga8DzgS+XvHYmcJe19mfW2jHyo742GWNWJlC+Ul8jVFZjTBdwOPD7hMoUdjjwZWvtndbaaWvttwqvn0u6zul85TyBFJ1Pa+0OYLW19lfGmAOAg8kPQEnd9R9RVkjo+s9CIqjJMhZxMMasIf8U+GljTJ8x5pfGmD9NulwFF1lrX03+CbUofG73AoPk54IkqVJZTwCGgR8Wzu1PEr4Z3Gmt/WAxLvydO8mfu9Sc04hyNpOi8wlgrR0yxjwP2Ad8DLiYlF7/lcqa5PWfhURQ9TIWCToY+BnwCeDZwFeA7xtjDkqyUADW2j9UeDmV5zairL8kP1T5MOBu8k0cLbUsWyXGmKOAG4F/IN/enrpzCmXlbCGd53Mb0A68i3xZV5DS80l5WY8ioes/C53FNVnGIg7W2q3AX5a89BVjzPuAFwG3JlOqSPV0bn8C/KQYG2P+CbgQOBb4bULFwhjzQuD7wJXW2iuMMZ8jhec0XM7Cy6k7n9baXOH/Xl0YLJDaf6MVynqItTaR6z8LNYK6WcbCGPMiY8y7Qy+3AmNJlMdD+NweTL5D8aHESjQPY8yrjTFvKHmpifyDUGLn1hhzKvnOy7+z1v5T4eXUndNK5Uzb+TTGvMwY8+PQy62k8PqPKOuGpK7/LNQI6mkZi3FgszFmK/lq9/nkq44/T7RU87sFuMwY83LgLvJDDG+11ib+tFVBK/BZY8x9wCPk22W3WmsTWWPbGHM0cAP5oYw3lryVqnMaUc5UnU9gC/CCQnK6gXxzSzP54bdpu/7nK+vPgR8lcf0v+xpBPS1jYa39Nfk//teAZ4DXA68ujB5JHWvtk+TL+FlgD/l2zXckWqh5WGu/C1wB3E7+38HxwNkJFumd5Pstvm6MGSr+B6wnXed0vnL2kaLzaa19GngN8GHgaeC1wGlpvP4jynoXCV3/WmJCRCTjln2NQEREoikRiIhknBKBiEjGKRGIiGScEoGISMYpEYiIZJwSgcgiGWN+VlhiQaQuKRGIiGRcFpaYEKkJY8xbye8uNQD8MfA2a+1NiRZKxIMSgUi8XgC8EXh10gUR8aVEIBKvMeDb1lqt3SJ1Q30EIvHaoyQg9UaJQCReSgJSd5QIREQyTolARCTjtB+BiEjGqUYgIpJxSgQiIhmnRCAiknFKBCIiGadEICKScUoEIiIZp0QgIpJxSgQiIhmnRCAiknH/HxfY0ApfQMKGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var = 'Ir'\n",
    "data = pd.concat([train_data['pm2.5_log'], train_data[var]], axis=1)\n",
    "data.plot.scatter(x=var, y='pm2.5_log', ylim=(0, 10));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "散点图分布看，风速和降雨好像和pm2.5关系还挺大。其他特征没发现太大关系。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1152 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var = 'DEWP'\n",
    "data = pd.concat([train_data['pm2.5_log'], train_data[var]], axis=1)\n",
    "f, ax = plt.subplots(figsize=(20, 16))\n",
    "fig = sns.boxplot(x=var, y=\"pm2.5_log\", data=data)\n",
    "fig.axis(ymin=0, ymax=10);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们用箱线图看看露点和pm2.5的关系，好吧，我也不知道我想看到什么，或许我只是想画个箱线图吧，黑线脸。。。\n",
    "我们还是看看想关性矩阵吧"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x23466940>"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#correlation matrix\n",
    "corrmat = train_data.corr()\n",
    "f, ax = plt.subplots(figsize=(12, 8))\n",
    "sns.heatmap(corrmat, vmax=0.8, square=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们详细看看正相关和负相关的5个特征，pm2.5特征应该干掉的。留着就留着吧，反正我后面不会用它，我就是用它来看看log值他俩到底有多相关。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [],
   "source": [
    "k = 5 #number of variables for heatmap\n",
    "cols_large = corrmat.nlargest(k, 'pm2.5_log')['pm2.5_log'].index \n",
    "cols_small = corrmat.nsmallest(k, 'pm2.5_log')['pm2.5_log'].index "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['pm2.5_log', 'pm2.5', 'DEWP', 'cbwd_SE', 'cbwd_cv', 'Iws', 'cbwd_NW',\n",
       "       'PRES', 'cbwd_NE', 'Ir'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols =cols_large.append(cols_small)\n",
    "cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {},
   "outputs": [],
   "source": [
    "cm = np.corrcoef(train_data[cols].values.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set(rc = {\"figure.figsize\":(12,10)}) \n",
    "sns.set(font_scale=1.25)\n",
    "hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size':10}, yticklabels=cols.values, xticklabels=cols.values)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5.线性模型尝试"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们先把数据类型改为float32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data['hour']=train_data['hour'].astype('float32')\n",
    "train_data['DEWP']=train_data['DEWP'].astype('float32')\n",
    "train_data['TEMP']=train_data['TEMP'].astype('float32')\n",
    "train_data['PRES']=train_data['PRES'].astype('float32')\n",
    "train_data['Iws']=train_data['Iws'].astype('float32')\n",
    "train_data['Is']=train_data['Is'].astype('float32')\n",
    "train_data['Ir']=train_data['Ir'].astype('float32')\n",
    "train_data['cbwd_NE']=train_data['cbwd_NE'].astype('float32')\n",
    "train_data['cbwd_NW']=train_data['cbwd_NW'].astype('float32')\n",
    "train_data['cbwd_SE']=train_data['cbwd_SE'].astype('float32')\n",
    "train_data['cbwd_cv']=train_data['cbwd_cv'].astype('float32')\n",
    "train_data['year']=train_data['year'].astype('float32')\n",
    "train_data['month']=train_data['month'].astype('float32')\n",
    "train_data['day']=train_data['day'].astype('float32')\n",
    "train_data['week']=train_data['week'].astype('float32')\n",
    "train_data['pm2.5_log']=train_data['pm2.5_log'].astype('float32') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_data['hour']=test_data['hour'].astype('float32')\n",
    "test_data['DEWP']=test_data['DEWP'].astype('float32')\n",
    "test_data['TEMP']=test_data['TEMP'].astype('float32')\n",
    "test_data['PRES']=test_data['PRES'].astype('float32')\n",
    "test_data['Iws']=test_data['Iws'].astype('float32')\n",
    "test_data['Is']=test_data['Is'].astype('float32')\n",
    "test_data['Ir']=test_data['Ir'].astype('float32')\n",
    "test_data['cbwd_NE']=test_data['cbwd_NE'].astype('float32')\n",
    "test_data['cbwd_NW']=test_data['cbwd_NW'].astype('float32')\n",
    "test_data['cbwd_SE']=test_data['cbwd_SE'].astype('float32')\n",
    "test_data['cbwd_cv']=test_data['cbwd_cv'].astype('float32')\n",
    "test_data['year']=test_data['year'].astype('float32')\n",
    "test_data['month']=test_data['month'].astype('float32')\n",
    "test_data['day']=test_data['day'].astype('float32')\n",
    "test_data['week']=test_data['week'].astype('float32') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['date', 'hour', 'pm2.5', 'DEWP', 'TEMP', 'PRES', 'Iws', 'Is', 'Ir',\n",
       "       'cbwd_NE', 'cbwd_NW', 'cbwd_SE', 'cbwd_cv', 'year', 'month', 'day',\n",
       "       'week', 'pm2.5_log'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. 所有数值型变量都作为特征进行线性回归。我自己算的mse值和dcrace上不一致，暂时还不明白为什么。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 0.59\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.metrics import mean_squared_error \n",
    "\n",
    "# train_data.drop(['date'], axis=1, inplace=True)\n",
    "y=train_data['pm2.5_log']\n",
    "var =['hour','DEWP', 'TEMP', 'PRES', 'Iws', 'Is', 'Ir',\n",
    "       'cbwd_NE', 'cbwd_NW', 'cbwd_SE', 'cbwd_cv', 'year', 'month', 'day',\n",
    "       'week'] \n",
    "X=train_data[var]\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "reg = LinearRegression().fit(X_train, y_train) \n",
    "y_val_pre = reg.predict(X_val) \n",
    "y_val1= y_val.reset_index(drop=True)\n",
    "print(\"Mean squared error: %.2f\" % mean_squared_error(y_val1, y_val_pre ))  \n",
    "df1 = pd.DataFrame (y_val_pre, columns = ['p'])  \n",
    "df1['r'] = y_val \n",
    "df1.to_csv(\"test1.csv\",encoding = \"utf-8\",header=1,index=0)\n",
    "\n",
    "X_test = test_data[var]\n",
    "y_test=reg.predict(X_test)\n",
    "y_rel = np.round(np.exp(y_test)) \n",
    "df = pd.DataFrame (y_rel, columns = ['pm2.5'])\n",
    "df.to_csv(\"sample.csv\",encoding = \"utf-8\",header=1,index=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.375"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error\n",
    "y_true = [3, -0.5, 2, 7]\n",
    "y_pred = [2.5, 0.0, 2, 8]\n",
    "mean_squared_error(y_true, y_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2.按照相关性选择特征，结果更差。dcrace上好于第一种。。。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean squared error: 0.80\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.metrics import mean_squared_error \n",
    "\n",
    "# train_data.drop(['date'], axis=1, inplace=True)\n",
    "y=train_data['pm2.5_log']\n",
    "var =['DEWP',  'Iws',  'Ir', 'cbwd_NE', 'cbwd_NW', 'cbwd_SE', 'cbwd_cv']\n",
    "X=train_data[var]\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "reg = LinearRegression().fit(X_train, y_train) \n",
    "y_val_pre = reg.predict(X_val)\n",
    "print(\"Mean squared error: %.2f\" % mean_squared_error(y_val, y_val_pre))\n",
    "X_test = test_data[var]\n",
    "y_test=reg.predict(X_test)\n",
    "y_rel = np.round(np.exp(y_test)) \n",
    "df.to_csv(\"sample_select.csv\",encoding = \"utf-8\",header=1,index=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3.用pca进行降维处理，效果更差。但dcrace上mse为0了。。。难以理解，期待大神指点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.80673332 0.12615844 0.02586577]\n",
      "Mean squared error: 0.88\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=3)\n",
    "pca.fit(X)\n",
    "X_pca=pca.transform(X)\n",
    "print(pca.explained_variance_ratio_)  \n",
    "X_train, X_val, y_train, y_val = train_test_split(X_pca, y, test_size=0.2, random_state=42)\n",
    "reg_pca = LinearRegression().fit(X_train, y_train) \n",
    "y_val_pre = reg_pca.predict(X_val)\n",
    "print(\"Mean squared error: %.2f\" % mean_squared_error(y_val, y_val_pre))\n",
    "\n",
    "X_test = test_data[var]\n",
    "X_test_pca=pca.transform(X_test)\n",
    "y_test=reg_pca.predict(X_test_pca)\n",
    "y_rel = np.round(np.exp(y_test)) \n",
    "df = pd.DataFrame (y_rel, columns = ['pm2.5'])\n",
    "df.to_csv(\"sample_pca.csv\",encoding = \"utf-8\",header=1,index=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 记录下标准化用法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.6978544 ],\n",
       "       [0.83116937],\n",
       "       [0.90072984],\n",
       "       [1.0264692 ],\n",
       "       [0.7632907 ],\n",
       "       [0.534399  ],\n",
       "       [0.49812275],\n",
       "       [0.659499  ],\n",
       "       [0.6276842 ],\n",
       "       [0.72016066]], dtype=float32)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "saleprice_scaled = StandardScaler().fit_transform(train_data['pm2.5_log'][:,np.newaxis]);\n",
    "saleprice_scaled[0:10]"
   ]
  }
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